Get A/B Test Results

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the tool returns detailed results including winner, confidence, and per-cell metrics, which informs the agent about the data shape. No contradictions with annotations. The response_format parameter is explained, adding behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a single sentence stating purpose, followed by clear Args and Returns sections. Every element earns its place, and the main purpose is front-loaded. No unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with two parameters and no output schema, the description is fairly complete. It explains the return content (winner, confidence, per-cell metrics) and the optional response_format. However, it could mention that the study must exist and be completed, but this is implied by 'detailed results' and the 'get' verb. Overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (both parameters described), so baseline is 3. The description's Args section reiterates schema info without new details, but the Returns section adds meaning by explaining what the study_id parameter will retrieve (winner, confidence, per-cell metrics). This helps the agent understand the tool's output beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets detailed results of a specific A/B test (ad study). It specifies the resource (results) and distinguishes from siblings like meta_get_ad_studies (list studies) and meta_get_ad_insights (general insights). The verb 'Gets' is appropriate, and the description includes return details (winner, confidence, per-cell metrics) for clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives (e.g., meta_get_ad_studies to find available studies), nor does it mention prerequisites or when not to use. This is a significant gap for an agent to select the tool correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.